Integration of machine learning and crop modelling can optimize predictions of plant growth and yield.
Identification of traits affecting pollen performance
Introducing cageminer, an R/Bioconductor package to prioritize candidate genes by integrating GWAS and gene coexpression networks
The best method depends on your goal, need for accuracy, and computing allowance
An assessment of deep generative networks in creating realistic 3D data
Surveying machine learning methods to improve detection of binding sites.
Predicting phenology can provide insight into better management.
Bioengineering boosts photosynthesis and increases yields in food crops for the first time ever.
A model designed to study circadian effects on physiology becomes more broadly useful when the clock regulates metabolites.
A new gene-to-phenotype network for shoot branching.
Measuring the impact of brace roots on lodging resistance in maize.
The closeness of plants affects architectural plasticity and carbon uptake.
A new study uses computational modelling to determine how and why cotton quality develops.
A new study highlights areas of compatibility and strategies to move towards better communication and collaboration.